Senior Applied AI Solutions Engineer

ppbThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied AI Solutions Engineer based in Italy. /b /p pThis role sits at the intersection of advanced ML engineering, customer success, and product development within a rapidly evolving AI infrastructure environment.br/You will help enterprise customers move complex machine learning workloads from proof of concept into production.br/Around half of your time will be spent working directly with customers, solving technical challenges and accelerating their adoption of the platform.br/The remainder will focus on building prototypes, exploring emerging AI techniques, and demonstrating new possibilities across the product portfolio.br/Your hands-on field experience will directly influence product priorities, helping transform recurring customer challenges into concrete improvements.br/You will work across areas including inference, databases, MLOps, serverless AI, agentic architectures, and specialized AI applications.br/This is a highly autonomous role for an engineer who wants technical depth, customer impact, and a direct voice in the evolution of AI products. /p h3Accountabilities /h3 ul lipBuild polished prototypes and technical demonstrations across serverless inference, databases, MLflow, MLOps, and applied AI use cases, including Physical AI and healthcare and life sciences. /p /li lipSupport enterprise customers hands-on through proof-of-concept design, technical onboarding, validation, and the transition from experimentation to production. /p /li lipAct as a technical bridge between customer ML teams and the platform, helping diagnose challenges and accelerate time-to-value during the first months of adoption. /p /li lipResearch emerging applied AI techniques, including new training approaches, inference optimizations, agentic architectures, and frameworks, and turn relevant discoveries into working prototypes. /p /li lipTranslate research and customer experience into technical write-ups, recommendations, and actionable product feedback. /p /li lipIdentify recurring issues across customer deployments and provide specific, evidence-based input that can shape the product roadmap. /p /li lipDevelop reusable technical assets such as notebooks, reference architectures, benchmarks, and implementation guides to reduce onboarding friction. /p /li lipCollaborate with sales, product, engineering, and customer teams to ensure technical insights are converted into practical solutions and product improvements. /p /li lipTrack developments across the AI ecosystem and help the wider team anticipate important applied AI trends over the next 6–12 months. /p /li /ul h3Requirements /h3 ul lipStrong hands-on experience with modern machine learning systems, including fine-tuning large models, debugging distributed training workloads, building production RAG or agentic pipelines, and optimizing GPU-based inference. /p /li lipFluency across the modern ML technology stack, including PyTorch, Hugging Face, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent platforms, and vector databases. /p /li lipExperience working directly with enterprise ML teams, whether in a solutions engineering, customer engineering, ML engineering, or closely related capacity. /p /li lipDemonstrated ability to research emerging techniques, read technical and academic papers, and translate relevant findings into working implementations. /p /li lipStrong understanding of ML infrastructure and the ability to troubleshoot complex workloads across the stack. /p /li lipExcellent communication skills, with the ability to adapt technical explanations for audiences ranging from ML engineers to senior technology and business leaders. /p /li lipStrong customer orientation combined with the ability to maintain technical depth and engineering rigor. /p /li lipAbility to work effectively in a highly varied environment, moving between customer problems, technical research, rapid prototyping, and product feedback. /p /li lipExperience in Physical AI, robotics, simulation, healthcare and life sciences, drug discovery, medical imaging, clinical NLP, or enterprise AI application development is an advantage. /p /li lipFamiliarity with large-scale MLOps technologies such as Kubeflow, Metaflow, Argo, or Ray is a plus. /p /li lipPrevious experience at a cloud provider or AI infrastructure company is beneficial. /p /li lipPublicly shared technical work, such as useful notebooks, talks, technical articles, or open-source contributions, is a strong plus. /p /li /ul h3Benefits /h3 ul lipCompetitive compensation, with the advertised base compensation range of b$200,000–$350,000 USD /b, depending on experience, skills, qualifications, level, and location. /p /li lipComprehensive benefits package. /p /li lipFlexible working arrangements with significant ownership and autonomy. /p /li lipProfessional growth, continuous learning, and career development opportunities. /p /li lipOpportunity to work on impactful AI and ML infrastructure projects. /p /li lipCollaborative, innovative, and fast-moving working environment. /p /li lipInternational environment with talented teams distributed across multiple locations. /p /li lipOpportunity to work directly with enterprise customers and influence product direction. /p /li lipExposure to emerging areas of applied AI, including generative AI, agentic systems, MLOps, inference optimization, Physical AI, and healthcare applications. /p /li lipAn environment that values initiative, technical excellence, trust, and meaningful ownership. /p /li /ul pInclusive workplace committed to equal employment opportunities and a diverse, supportive culture. /p /p #J-18808-Ljbffr